In this work, we introduce an alternative perspective based on the relationship between messages an agent produces and the messages it receives. This leads to a simple method based on contrastive learning to ground communication. Precisely, inspired by literature in representation learning across different views of a data sample [1]}, for a given trajectory, we propose viewing messages sent across agents to be different incomplete views of environment states. From this perspective, messages in a trajectory should be more coherently constructed than messages sent in another trajectory. Hence, we propose using a contrastive learning method to align the message space for communication (i.e. ground communication) which pulls messages within a trajectory to be closer to each other and pushes messages of different trajectories to be further apart. We evaluate our method in communication-essential environments and empirically show how our method leads to improved speed and performance with a greater resemblance of a common language than existing methods, without additional learning parameters.
